Detecting Colors (Hsv Color Space) - Opencv With Python - Pysource

Detecting colors (Hsv Color Space) – Opencv with Python Click here to download source code

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In this lesson, we will analyze a basic but important tool for identifying colors through a mask. We’re going to see in this video how to detect colors through HSV Color space on Opencv with Python.

HSV corresponds to:Hue is the colorSaturation is the greynessValue is the brightness

Understanding the concepts of balancing these three elements, we can implement a basic object recognition based on colors. In this tutorial, I will explain in a few steps how to create a mask to balance the recognition of our object in real-time.

We import the libraries Opencv and Numpy, then load the cap to get the frames from the webcam. After that we start a while Loop where we get the frames and we do the detection.

import cv2 import numpy as np cap = cv2.VideoCapture(0) while True: _, frame = cap.read() hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

Inside the while loop we define the HSV ranges (low_red, high_red), we create the mask and we show only the object with the red color.

# Red color low_red = np.array([161, 155, 84]) high_red = np.array([179, 255, 255]) red_mask = cv2.inRange(hsv_frame, low_red, high_red) red = cv2.bitwise_and(frame, frame, mask=red_mask)

Same for the other colors:

# Blue color low_blue = np.array([94, 80, 2]) high_blue = np.array([126, 255, 255]) blue_mask = cv2.inRange(hsv_frame, low_blue, high_blue) blue = cv2.bitwise_and(frame, frame, mask=blue_mask) # Green color low_green = np.array([25, 52, 72]) high_green = np.array([102, 255, 255]) green_mask = cv2.inRange(hsv_frame, low_green, high_green) green = cv2.bitwise_and(frame, frame, mask=green_mask) # Every color except white low = np.array([0, 42, 0]) high = np.array([179, 255, 255]) mask = cv2.inRange(hsv_frame, low, high) result = cv2.bitwise_and(frame, frame, mask=mask)

We finally show the result:

cv2.imshow("Frame", frame) cv2.imshow("Red", red) cv2.imshow("Blue", blue) cv2.imshow("Green", green) cv2.imshow("Result", result) key = cv2.waitKey(1) if key == 27: break

Object recognition based on colors

In this article you have learned, I hope without too many worries, the important concept of OpenCV for basic object recognition in computer vision.For a deeper understanding of the topic and to have a greater mastery of the subject, I suggest you evaluate the purchase of my Object Detection course.

Sergio Canu

Hi there, I’m the founder of Pysource. I’m a Computer Vision Consultant, developer and Course instructor. I help Companies and Developers to build efficient computer vision software.

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